class_docstr: The citation datasets Cora, Citeseer and Pubmed.
  
  Node attributes are bag-of-words vectors representing the most common words
  in the text document associated to each node.
  Two papers are connected if either one cites the other.
  Labels represent the subject area of the paper.
  
  The train, test, and validation splits are given as binary masks and are
  accessible via the `mask_tr`, `mask_va`, and `mask_te` attributes.
  
  **Arguments**
  
  - `name`: name of the dataset to load (`'cora'`, `'citeseer'`, or
  `'pubmed'`);
  - `random_split`: if True, return a randomized split (20 nodes per class
  for training, 30 nodes per class for validation and the remaining nodes for
  testing, as recommended by [Shchur et al. (2018)](https://arxiv.org/abs/1811.05868)).
  If False (default), return the "Planetoid" public splits defined by
  [Yang et al. (2016)](https://arxiv.org/abs/1603.08861).
  - `normalize_x`: if True, normalize the features.
  - `dtype`: numpy dtype of graph data.
code: def available_datasets():
      return ["cora", "citeseer", "pubmed"]
  
imports: import os
  import os.path
  import networkx
  import numpy
  import requests
  import scipy.sparse
  from sklearn.model_selection import train_test_split
  from spektral.data import Dataset, Graph
  from spektral.datasets.utils import DATASET_FOLDER
  from spektral.utils.io import load_binary
label_desc: Use image node_id 5 for calling the Citation obj's underlying member method code with example usage: obj.available_datasets() and returns: list